Contextual Document Similarity for Content-based Literature Recommender Systems

TL;DR

This paper introduces a novel approach to document similarity that incorporates contextual facets, enhancing content-based recommender systems by allowing more precise and facet-aware document recommendations.

Contribution

It develops and evaluates a new method for measuring contextual document similarity, addressing the limitations of traditional similarity measures in digital libraries.

Findings

Contextual similarity improves recommendation relevance.

Neural approaches and semantic features enhance similarity measurement.

Faceted similarity enables more nuanced exploration of document collections.

Abstract

To cope with the ever-growing information overload, an increasing number of digital libraries employ content-based recommender systems. These systems traditionally recommend related documents with the help of similarity measures. However, current document similarity measures simply distinguish between similar and dissimilar documents. This simplification is especially crucial for extensive documents, which cover various facets of a topic and are often found in digital libraries. Still, these similarity measures neglect to what facet the similarity relates. Therefore, the context of the similarity remains ill-defined. In this doctoral thesis, we explore contextual document similarity measures, i.e., methods that determine document similarity as a triple of two documents and the context of their similarity. The context is here a further specification of the similarity. For example, in the scientific domain, research papers can be similar with respect to their background, methodology, or findings. The measurement of similarity in regards to one or more given contexts will enhance recommender systems. Namely, users will be able to explore document collections by formulating queries in terms of documents and their contextual similarities. Thus, our research objective is the development and evaluation of a recommender system based on contextual similarity. The underlying techniques will apply established similarity measures and as well as neural approaches while utilizing semantic features obtained from links between documents and their text.